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e":["from google.colab import drive\n","drive.mount('/content/drive')"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}]},{"cell_type":"code","metadata":{"id":"SjDdD4vHN1D6"},"source":["import os\n","os.chdir('/content/drive/MyDrive/transformers/天池-入门NLP - 新闻文本分类')"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"laWQqNegpwhR"},"source":["#安装\n","!pip install transformers datasets"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"-tf3bg_DPfgx"},"source":["# 文件读取\n","import pandas as pd\n","from datasets import load_dataset\n","from datasets import Dataset\n","\n","train_df=pd.read_csv('./train_set.csv',sep='\\t')\n","test_df=pd.read_csv('./test_a.csv', sep ='\\t')\n","df=pd.concat((train_df,test_df))"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"TnHEQBaqTLIc"},"source":["#将3750/648/900改成标点符号，删除原text列，新增列重名为text列\n","import re\n","def replacepunc(x):\n","  x=re.sub('3750',\",\",x)\n","  x=re.sub('900',\".\",x)\n","  x=re.sub('648',\"!\",x)\n","  return x\n","df['words']=df['text'].map(lambda x: replacepunc(x))\n","df.drop('text',axis=1,inplace=True)\n","df.columns=['label','text']\n","\n","#数据载入dataset，去除多余的列，只保留text列\n","data=Dataset.from_pandas(df).remove_columns(['label', '__index_level_0__'])\n","data"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"Vs1cPtqjpSDQ"},"source":["batch_size=1000\n","#all_texts=[data['text'][i:i+batch_size] for i in range(0,len(data),batch_size)]\n","\n","def batch_iterator():\n","  for i in range(0,len(data),batch_size):\n","    yield data['text'][i:i+batch_size]"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"x49jbjyuo5Ai"},"source":["#初始化分词器、预分词器\n","from tokenizers import decoders,models,normalizers,pre_tokenizers,processors,trainers,Tokenizer\n","\n","tokenizer=Tokenizer(models.WordPiece(unl_token=\"[UNK]\"))\n","\n","tokenizer.pre_tokenizer=pre_tokenizers.BertPreTokenizer()\n","special_tokens=[\"[UNK]\", \"[PAD]\", \"[CLS]\", \"[SEP]\", \"[MASK]\"]\n","trainer=trainers.WordPieceTrainer(vocab_size=7000,min_frequency=2,special_tokens=[\"[UNK]\",\"[CLS]\",\"[SEP]\",\"[PAD]\",\"[MASK]\"])\n","tokenizer.decoders=decoders.WordPiece(prefix=\"##\")"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"6ZY816xowl81"},"source":["#开始训练\n","tokenizer.train_from_iterator(batch_iterator(),trainer=trainer)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"vDGR8jOrwrRh"},"source":["#进行分词后处理\n","cls_token_id = tokenizer.token_to_id(\"[CLS]\")\n","sep_token_id = tokenizer.token_to_id(\"[SEP]\")\n","mask_token_id = tokenizer.token_to_id(\"[MASK]\")\n","pad_token_id = tokenizer.token_to_id(\"[PAD]\")\n","\n","tokenizer.post_processor = processors.TemplateProcessing(\n","    single=f\"[CLS]:0 $A:0 [SEP]:0\",\n","    pair=f\"[CLS]:0 $A:0 [SEP]:0 $B:1 [SEP]:1\",\n","    special_tokens=[(\"[CLS]\",cls_token_id),(\"[SEP]\",sep_token_id),(\"[MASK]\",mask_token_id)],\n","    )\n","\n","tokenizer.enable_truncation(max_length=512)\n","tokenizer.enable_padding(pad_token='[PAD]')\n"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"pnXOenT-wz3M"},"source":["#测试分词结果\n","encoding = tokenizer.encode('2491 4109 1757 7539 648 3695 3038 4490 23 7019 3731 4109 3792 2465',' 2893 7212 5296 1667 3618 7044 1519 5413 1283 6122 4893 7495 2435 5510')\n","encoding.tokens"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"JYx0116UxIgj"},"source":["#保存模型并重新加载\n","#tokenizer.save(\"tokenizers.json\")\n","\n","from transformers import PreTrainedTokenizerFast\n","fast_tokenizer = PreTrainedTokenizerFast(tokenizer_file=\"tokenizers.json\",\n","   model_max_length=512,mask_token='[MASK]',pad_token='[PAD]',unk_token='[UNK]',\n","   cls_token='[CLS]',sep_token='[SEP]',padding_side='right',\n","   return_special_tokens_mask=True)\n","#PreTrainedTokenizerFast中一定要设置mask_token，pad_token等，不然报错"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"6M5J86LWIMKX"},"source":["#data_collator是一个函数，负责获取样本并将它们批处理成张量\n","#在data_collator中可以确保每次以新的方式完成随机掩蔽。\n","from transformers import DataCollatorForLanguageModeling\n","data_collator=DataCollatorForLanguageModeling(tokenizer=fast_tokenizer,mlm=True,mlm_probability=0.15)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"f0f9hRCJWA6u"},"source":["#加载训练中保存的模型，继续训练\n","from transformers import BertForMaskedLM\n","model = BertForMaskedLM.from_pretrained('./Test-Clm/checkpoint-18000')"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"jHl6Ru5sxU62"},"source":["#初始化bert模型\n","from transformers import BertConfig\n","config = BertConfig(\n","    vocab_size=7000,\n","    hidden_size=512,\n","    intermediate_size=4*512,\n","    max_position_embeddings=512,\n","    num_hidden_layers=4,\n","    num_attention_heads=4,\n","    type_vocab_size=2,\n","    attention_probs_dropout_prob=0.1,\n","    hidden_dropout_prob=0.1,\n","    initializer_range=0.02\n",")\n","\n","from transformers import BertForMaskedLM\n","model = BertForMaskedLM(config=config)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"7JSzZ9jbk-o5","colab":{"base_uri":"https://localhost:8080/","height":116,"referenced_widgets":["6ff37bfc03874d9298e5a4a3baf1c0c4","b390f2fbec504a5184cd2db7dc1e41a7","d049ea5cc1f344f6ae2dc05ca60f5d6e","ac47b5ddd6604956a3599d6bef31fc8d","49b533c3f31e42659021e1e88476272e","181c08e96fdf4134a05910d16e92a163","d316ad3e386646f4aa49039dd2c53e12","d72821bcf2834bc58b40b7ec19bbcef6","e3765dd6f52440eda0a6ed9c129f87bc","fdb5b0fc3b894ab6b21722f594ffc50e","16b2f413cbfa45d0a142d7d600c8dcb8"]},"executionInfo":{"status":"ok","timestamp":1634734683850,"user_tz":-480,"elapsed":373022,"user":{"displayName":"张hongxu","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"01344108933923387301"}},"outputId":"a2c134c2-43f7-404b-bae1-c7c11ece5c52"},"source":["#数据进行分词预处理，删除‘text'列，否则后面拼接的时候会报错。\n","tokenized_datasets=data.map(lambda examples:fast_tokenizer(examples['text']),batched=True).remove_columns(\"text\")\n","\n","#tokenized_datasets.save_to_disk('./tokenized_datasets')\n","#from datasets import load_from_disk\n","#tokenized_datasets=load_from_disk('./tokenized_datasets')\n","tokenized_datasets"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"6ff37bfc03874d9298e5a4a3baf1c0c4","version_minor":0,"version_major":2},"text/plain":["  0%|          | 0/250 [00:00<?, ?ba/s]"]},"metadata":{}},{"output_type":"execute_result","data":{"text/plain":["Dataset({\n","    features: ['attention_mask', 'input_ids', 'text', 'token_type_ids'],\n","    num_rows: 250000\n","})"]},"metadata":{},"execution_count":100}]},{"cell_type":"code","metadata":{"id":"SG-OW5W7Isj6"},"source":["\"\"\"def tokenize_function(examples):\n","    return fast_tokenizer (examples[\"text\"])\n","tokenized_datasets = dataset.map(tokenize_function,batched=True,remove_columns=[\"text\"])\"\"\""],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"AuMmpZ2LIwy8"},"source":["block_size = 128\n","def group_texts(examples):\n","  # 拼接所有文本\n","  concatenated_examples={k:sum(examples[k],[]) for k in examples.keys()}\n","  total_length=len(concatenated_examples[list(examples.keys())[0]])\n","  # 我们将余数对应的部分去掉。但如果模型支持的话，可以添加padding，您可以根据需要定制此部件。\n","  total_length = (total_length//block_size)*block_size\n","  # 通过max_len进行分割。\n","  result={\n","      k:[t[i:i+block_size] for i in range(0,total_length,block_size)]\n","      for k,t in concatenated_examples.items()\n","  }\n","  result[\"labels\"]=result[\"input_ids\"].copy()\n","  return result\n","\n","lm_datasets=tokenized_datasets.map(\n","    group_texts,\n","    batched=True,\n","    batch_size=1000,\n","    num_proc=4,\n",")"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"UVJCAvvLXU3r"},"source":["#加载和保存拼接后的文本\n","#lm_datasets.save_to_disk('./lm_datasets')\n","\n","import pandas as pd\n","from datasets import load_from_disk\n","lm_datasets=load_from_disk('./lm_datasets')"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"jC2-OjE02Zc7","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1634737721760,"user_tz":-480,"elapsed":431,"user":{"displayName":"张hongxu","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"01344108933923387301"}},"outputId":"a2e18642-f741-420e-f9ee-3541d9b2e11a"},"source":["lm_datasets"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Dataset({\n","    features: ['attention_mask', 'input_ids', 'labels', 'token_type_ids'],\n","    num_rows: 1779535\n","})"]},"metadata":{},"execution_count":8}]},{"cell_type":"code","metadata":{"id":"vBQtcuRB_miY","colab":{"base_uri":"https://localhost:8080/","height":764},"executionInfo":{"status":"ok","timestamp":1634737751366,"user_tz":-480,"elapsed":23939,"user":{"displayName":"张hongxu","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"01344108933923387301"}},"outputId":"486d4acd-9579-40df-bd67-b642f7fa735a"},"source":["import os\n","assert os.environ['COLAB_TPU_ADDR']\n","#'Make sure to select TPU from Edit > Notebook settings > Hardware accelerator'\n","\n","!pip install cloud-tpu-client==0.10 https://storage.googleapis.com/tpu-pytorch/wheels/torch_xla-1.9-cp37-cp37m-linux_x86_64.whl"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting torch-xla==1.9\n","  Downloading https://storage.googleapis.com/tpu-pytorch/wheels/torch_xla-1.9-cp37-cp37m-linux_x86_64.whl (149.9 MB)\n","\u001b[K     |████████████████████████████████| 149.9 MB 22 kB/s \n","\u001b[?25hCollecting cloud-tpu-client==0.10\n","  Downloading cloud_tpu_client-0.10-py3-none-any.whl (7.4 kB)\n","Requirement already satisfied: oauth2client in /usr/local/lib/python3.7/dist-packages (from cloud-tpu-client==0.10) (4.1.3)\n","Collecting google-api-python-client==1.8.0\n","  Downloading google_api_python_client-1.8.0-py3-none-any.whl (57 kB)\n","\u001b[K     |████████████████████████████████| 57 kB 2.2 MB/s \n","\u001b[?25hRequirement already satisfied: google-api-core<2dev,>=1.13.0 in /usr/local/lib/python3.7/dist-packages (from google-api-python-client==1.8.0->cloud-tpu-client==0.10) (1.26.3)\n","Requirement already satisfied: google-auth>=1.4.1 in /usr/local/lib/python3.7/dist-packages (from google-api-python-client==1.8.0->cloud-tpu-client==0.10) (1.35.0)\n","Requirement already satisfied: six<2dev,>=1.6.1 in /usr/local/lib/python3.7/dist-packages (from google-api-python-client==1.8.0->cloud-tpu-client==0.10) (1.15.0)\n","Requirement already satisfied: google-auth-httplib2>=0.0.3 in /usr/local/lib/python3.7/dist-packages (from google-api-python-client==1.8.0->cloud-tpu-client==0.10) (0.0.4)\n","Requirement already satisfied: httplib2<1dev,>=0.9.2 in /usr/local/lib/python3.7/dist-packages (from google-api-python-client==1.8.0->cloud-tpu-client==0.10) (0.17.4)\n","Requirement already satisfied: uritemplate<4dev,>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from google-api-python-client==1.8.0->cloud-tpu-client==0.10) (3.0.1)\n","Requirement already satisfied: packaging>=14.3 in /usr/local/lib/python3.7/dist-packages (from google-api-core<2dev,>=1.13.0->google-api-python-client==1.8.0->cloud-tpu-client==0.10) (21.0)\n","Requirement already satisfied: protobuf>=3.12.0 in /usr/local/lib/python3.7/dist-packages (from google-api-core<2dev,>=1.13.0->google-api-python-client==1.8.0->cloud-tpu-client==0.10) (3.17.3)\n","Requirement already satisfied: googleapis-common-protos<2.0dev,>=1.6.0 in /usr/local/lib/python3.7/dist-packages (from google-api-core<2dev,>=1.13.0->google-api-python-client==1.8.0->cloud-tpu-client==0.10) (1.53.0)\n","Requirement already satisfied: pytz in /usr/local/lib/python3.7/dist-packages (from google-api-core<2dev,>=1.13.0->google-api-python-client==1.8.0->cloud-tpu-client==0.10) (2018.9)\n","Requirement already satisfied: setuptools>=40.3.0 in /usr/local/lib/python3.7/dist-packages (from google-api-core<2dev,>=1.13.0->google-api-python-client==1.8.0->cloud-tpu-client==0.10) (57.4.0)\n","Requirement already satisfied: requests<3.0.0dev,>=2.18.0 in /usr/local/lib/python3.7/dist-packages (from google-api-core<2dev,>=1.13.0->google-api-python-client==1.8.0->cloud-tpu-client==0.10) (2.23.0)\n","Requirement already satisfied: rsa<5,>=3.1.4 in /usr/local/lib/python3.7/dist-packages (from google-auth>=1.4.1->google-api-python-client==1.8.0->cloud-tpu-client==0.10) (4.7.2)\n","Requirement already satisfied: cachetools<5.0,>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from google-auth>=1.4.1->google-api-python-client==1.8.0->cloud-tpu-client==0.10) (4.2.4)\n","Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.7/dist-packages (from google-auth>=1.4.1->google-api-python-client==1.8.0->cloud-tpu-client==0.10) (0.2.8)\n","Requirement already satisfied: pyparsing>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>=14.3->google-api-core<2dev,>=1.13.0->google-api-python-client==1.8.0->cloud-tpu-client==0.10) (2.4.7)\n","Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /usr/local/lib/python3.7/dist-packages (from pyasn1-modules>=0.2.1->google-auth>=1.4.1->google-api-python-client==1.8.0->cloud-tpu-client==0.10) (0.4.8)\n","Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0dev,>=2.18.0->google-api-core<2dev,>=1.13.0->google-api-python-client==1.8.0->cloud-tpu-client==0.10) (3.0.4)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0dev,>=2.18.0->google-api-core<2dev,>=1.13.0->google-api-python-client==1.8.0->cloud-tpu-client==0.10) (2021.5.30)\n","Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0dev,>=2.18.0->google-api-core<2dev,>=1.13.0->google-api-python-client==1.8.0->cloud-tpu-client==0.10) (1.24.3)\n","Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0dev,>=2.18.0->google-api-core<2dev,>=1.13.0->google-api-python-client==1.8.0->cloud-tpu-client==0.10) (2.10)\n","Installing collected packages: google-api-python-client, torch-xla, cloud-tpu-client\n","  Attempting uninstall: google-api-python-client\n","    Found existing installation: google-api-python-client 1.12.8\n","    Uninstalling google-api-python-client-1.12.8:\n","      Successfully uninstalled google-api-python-client-1.12.8\n","\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n","earthengine-api 0.1.284 requires google-api-python-client<2,>=1.12.1, but you have google-api-python-client 1.8.0 which is incompatible.\u001b[0m\n","Successfully installed cloud-tpu-client-0.10 google-api-python-client-1.8.0 torch-xla-1.9\n"]},{"output_type":"display_data","data":{"application/vnd.colab-display-data+json":{"pip_warning":{"packages":["googleapiclient"]}}},"metadata":{}}]},{"cell_type":"code","metadata":{"id":"irCXJFhbCPF_"},"source":["import torch_xla.core.xla_model as xm\n","device = xm.xla_device()\n","model.to(device)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"28pLk7dUPLDF"},"source":["#使用GPU训练\n","import torch\n","device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n","model.to(device)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"X2NJr93zI8Wo","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1634738601702,"user_tz":-480,"elapsed":463,"user":{"displayName":"张hongxu","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"01344108933923387301"}},"outputId":"fb0f05bf-9a7d-41a0-b0be-1f50f5655769"},"source":["from transformers import Trainer, TrainingArguments\n","training_args = TrainingArguments(\n","    \"pre-mlm\",\n","    logging_strategy=\"steps\",\n","    logging_steps=3000,\n","    save_strategy=\"steps\",\n","    save_steps=9000,\n","    num_train_epochs=5,\n","    learning_rate=4e-4,\n","    per_device_train_batch_size=128,\n","    weight_decay=0.01\n",")\n","\n","trainer = Trainer(\n","    model=model,\n","    args=training_args,\n","    train_dataset=lm_datasets,\n","    data_collator=data_collator)"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["PyTorch: setting up devices\n","The default value for the training argument `--report_to` will change in v5 (from all installed integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as now. You should start updating your code and make this info disappear :-).\n"]}]},{"cell_type":"code","metadata":{"id":"Qlc3csuSJd6g","colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"status":"ok","timestamp":1634755227809,"user_tz":-480,"elapsed":16620401,"user":{"displayName":"张hongxu","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"01344108933923387301"}},"outputId":"ccfe15ed-5dc0-4558-a8a8-5892b3681602"},"source":["trainer.train()"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["***** Running training *****\n","  Num examples = 1779535\n","  Num Epochs = 5\n","  Instantaneous batch size per device = 128\n","  Total train batch size (w. parallel, distributed & accumulation) = 128\n","  Gradient Accumulation steps = 1\n","  Total optimization steps = 69515\n"]},{"output_type":"display_data","data":{"text/html":["\n","    <div>\n","      \n","      <progress value='69515' max='69515' style='width:300px; height:20px; vertical-align: middle;'></progress>\n","      [69515/69515 4:36:52, Epoch 5/5]\n","    </div>\n","    <table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: left;\">\n","      <th>Step</th>\n","      <th>Training Loss</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <td>3000</td>\n","      <td>4.056000</td>\n","    </tr>\n","    <tr>\n","      <td>6000</td>\n","      <td>2.624800</td>\n","    </tr>\n","    <tr>\n","      <td>9000</td>\n","      <td>2.407100</td>\n","    </tr>\n","    <tr>\n","      <td>12000</td>\n","      <td>2.283800</td>\n","    </tr>\n","    <tr>\n","      <td>15000</td>\n","      <td>2.201000</td>\n","    </tr>\n","    <tr>\n","      <td>18000</td>\n","      <td>2.136200</td>\n","    </tr>\n","    <tr>\n","      <td>21000</td>\n","      <td>2.084300</td>\n","    </tr>\n","    <tr>\n","      <td>24000</td>\n","      <td>2.038500</td>\n","    </tr>\n","    <tr>\n","      <td>27000</td>\n","      <td>2.001300</td>\n","    </tr>\n","    <tr>\n","      <td>30000</td>\n","      <td>1.965300</td>\n","    </tr>\n","    <tr>\n","      <td>33000</td>\n","      <td>1.931200</td>\n","    </tr>\n","    <tr>\n","      <td>36000</td>\n","      <td>1.904600</td>\n","    </tr>\n","    <tr>\n","      <td>39000</td>\n","      <td>1.879500</td>\n","    </tr>\n","    <tr>\n","      <td>42000</td>\n","      <td>1.855700</td>\n","    </tr>\n","    <tr>\n","      <td>45000</td>\n","      <td>1.826200</td>\n","    </tr>\n","    <tr>\n","      <td>48000</td>\n","      <td>1.806400</td>\n","    </tr>\n","    <tr>\n","      <td>51000</td>\n","      <td>1.787100</td>\n","    </tr>\n","    <tr>\n","      <td>54000</td>\n","      <td>1.770600</td>\n","    </tr>\n","    <tr>\n","      <td>57000</td>\n","      <td>1.750100</td>\n","    </tr>\n","    <tr>\n","      <td>60000</td>\n","      <td>1.733000</td>\n","    </tr>\n","    <tr>\n","      <td>63000</td>\n","      <td>1.715000</td>\n","    </tr>\n","    <tr>\n","      <td>66000</td>\n","      <td>1.705700</td>\n","    </tr>\n","    <tr>\n","      <td>69000</td>\n","      <td>1.695400</td>\n","    </tr>\n","  </tbody>\n","</table><p>"],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["Saving model checkpoint to pre-mlm/checkpoint-9000\n","Configuration saved in pre-mlm/checkpoint-9000/config.json\n","Model weights saved in pre-mlm/checkpoint-9000/pytorch_model.bin\n","Saving model checkpoint to pre-mlm/checkpoint-18000\n","Configuration saved in pre-mlm/checkpoint-18000/config.json\n","Model weights saved in pre-mlm/checkpoint-18000/pytorch_model.bin\n","Saving model checkpoint to pre-mlm/checkpoint-27000\n","Configuration saved in pre-mlm/checkpoint-27000/config.json\n","Model weights saved in pre-mlm/checkpoint-27000/pytorch_model.bin\n","Saving model checkpoint to pre-mlm/checkpoint-36000\n","Configuration saved in pre-mlm/checkpoint-36000/config.json\n","Model weights saved in pre-mlm/checkpoint-36000/pytorch_model.bin\n","Saving model checkpoint to pre-mlm/checkpoint-45000\n","Configuration saved in pre-mlm/checkpoint-45000/config.json\n","Model weights saved in pre-mlm/checkpoint-45000/pytorch_model.bin\n","Saving model checkpoint to pre-mlm/checkpoint-54000\n","Configuration saved in pre-mlm/checkpoint-54000/config.json\n","Model weights saved in pre-mlm/checkpoint-54000/pytorch_model.bin\n","Saving model checkpoint to pre-mlm/checkpoint-63000\n","Configuration saved in pre-mlm/checkpoint-63000/config.json\n","Model weights saved in pre-mlm/checkpoint-63000/pytorch_model.bin\n","\n","\n","Training completed. Do not forget to share your model on huggingface.co/models =)\n","\n","\n"]},{"output_type":"execute_result","data":{"text/plain":["TrainOutput(global_step=69515, training_loss=2.0477235787619468, metrics={'train_runtime': 16620.215, 'train_samples_per_second': 535.353, 'train_steps_per_second': 4.183, 'total_flos': 8.8070684806656e+16, 'train_loss': 2.0477235787619468, 'epoch': 5.0})"]},"metadata":{},"execution_count":20}]},{"cell_type":"code","metadata":{"id":"LRlBCECYMLHv","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1634755250392,"user_tz":-480,"elapsed":11044,"user":{"displayName":"张hongxu","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"01344108933923387301"}},"outputId":"4139311f-c1b0-46db-8b95-2d3dd87372a2"},"source":["#保存模型\n","trainer.save_model(\"./Pre_Bert\")"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["Saving model checkpoint to ./Pre_Bert\n","Configuration saved in ./Pre_Bert/config.json\n","Model weights saved in ./Pre_Bert/pytorch_model.bin\n"]}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":49,"referenced_widgets":["88a001709c2d4ad5a821145c5dcfee6b","3eb9d7a118114974acd8c1f39b7ff3eb","17190f95824e4e828eecc1d4d5f6f4ba","4bce05e9cd0646658face21212256af1","e2db8ab0233a494e828e9aebd9a1b8e1","5bba85c889324cf2acba78b012ecdbd0","e99c974b6b154c31b100eac23e4c83ae","8b3b45a1344e426c95267fafe1351206","3bfdc7fb15c1434f902deedcf96197fe","753ac9141be24a9593df09af8f3f3d42","1936586c0f7c451c9419c5ec847a8d1e"]},"id":"4hXP70j0CeHk","executionInfo":{"status":"ok","timestamp":1634755255881,"user_tz":-480,"elapsed":1122,"user":{"displayName":"张hongxu","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"01344108933923387301"}},"outputId":"23c7ec12-8c99-4ba1-bf3b-35d7510505da"},"source":["#准备进行下游任务微调\n","from datasets import load_metric\n","metric=load_metric(\"accuracy\")"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"88a001709c2d4ad5a821145c5dcfee6b","version_minor":0,"version_major":2},"text/plain":["Downloading:   0%|          | 0.00/1.42k [00:00<?, ?B/s]"]},"metadata":{}}]},{"cell_type":"code","metadata":{"id":"klkB8nUsVcIQ"},"source":["#加载训练好的预训练模型\n","from transformers import AutoModelForSequenceClassification\n","model=AutoModelForSequenceClassification.from_pretrained(\"./Pre_Bert\",num_labels=14)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"9gidK8_EEStZ"},"source":["#GPU训练\n","import torch\n","from transformers import  TrainingArguments, Trainer\n","device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n","model.to(device)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"wknpeP5gciD9"},"source":["#TPU训练\n","import torch_xla.core.xla_model as xm\n","device = xm.xla_device()\n","model.to(device)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"oOfSgLHtEA04"},"source":["import numpy as np\n","def compute_metrics(eval_pred):\n","  predictions, labels = eval_pred\n","  predictions = np.argmax(predictions, axis=1)\n","  \n","  return metric.compute(predictions=predictions, references=labels)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":417},"id":"kkBEFd1NejSu","executionInfo":{"status":"ok","timestamp":1634756340862,"user_tz":-480,"elapsed":12131,"user":{"displayName":"张hongxu","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"01344108933923387301"}},"outputId":"98a36e5e-2469-4aba-9ce0-c0b01090176e"},"source":["#加载数据\n","from datasets import Dataset\n","train_df=pd.read_csv('./train_set.csv',sep='\\t')\n","\n","\n","#准备将text文本首尾截断，各取255tokens\n","def slipt2(x):\n","  ls=x.split(' ')\n","  le=len(ls)\n","  if le<511:\n","    return x\n","  else:\n","    return ' '.join(ls[:255]+ls[-255:])\n","    \n","train_df\n","#small_df['summary']=small_df['text'].apply(lambda x:slipt2(x))"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>label</th>\n","      <th>text</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>2</td>\n","      <td>2967 6758 339 2021 1854 3731 4109 3792 4149 15...</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>11</td>\n","      <td>4464 486 6352 5619 2465 4802 1452 3137 5778 54...</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>3</td>\n","      <td>7346 4068 5074 3747 5681 6093 1777 2226 7354 6...</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>2</td>\n","      <td>7159 948 4866 2109 5520 2490 211 3956 5520 549...</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>3</td>\n","      <td>3646 3055 3055 2490 4659 6065 3370 5814 2465 5...</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>199995</th>\n","      <td>2</td>\n","      <td>307 4894 7539 4853 5330 648 6038 4409 3764 603...</td>\n","    </tr>\n","    <tr>\n","      <th>199996</th>\n","      <td>2</td>\n","      <td>3792 2983 355 1070 4464 5050 6298 3782 3130 68...</td>\n","    </tr>\n","    <tr>\n","      <th>199997</th>\n","      <td>11</td>\n","      <td>6811 1580 7539 1252 1899 5139 1386 3870 4124 1...</td>\n","    </tr>\n","    <tr>\n","      <th>199998</th>\n","      <td>2</td>\n","      <td>6405 3203 6644 983 794 1913 1678 5736 1397 191...</td>\n","    </tr>\n","    <tr>\n","      <th>199999</th>\n","      <td>3</td>\n","      <td>4350 3878 3268 1699 6909 5505 2376 2465 6088 2...</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>200000 rows × 2 columns</p>\n","</div>"],"text/plain":["        label                                               text\n","0           2  2967 6758 339 2021 1854 3731 4109 3792 4149 15...\n","1          11  4464 486 6352 5619 2465 4802 1452 3137 5778 54...\n","2           3  7346 4068 5074 3747 5681 6093 1777 2226 7354 6...\n","3           2  7159 948 4866 2109 5520 2490 211 3956 5520 549...\n","4           3  3646 3055 3055 2490 4659 6065 3370 5814 2465 5...\n","...       ...                                                ...\n","199995      2  307 4894 7539 4853 5330 648 6038 4409 3764 603...\n","199996      2  3792 2983 355 1070 4464 5050 6298 3782 3130 68...\n","199997     11  6811 1580 7539 1252 1899 5139 1386 3870 4124 1...\n","199998      2  6405 3203 6644 983 794 1913 1678 5736 1397 191...\n","199999      3  4350 3878 3268 1699 6909 5505 2376 2465 6088 2...\n","\n","[200000 rows x 2 columns]"]},"metadata":{},"execution_count":38}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"lK3Hbdi2G2t2","executionInfo":{"status":"ok","timestamp":1634757468005,"user_tz":-480,"elapsed":1210,"user":{"displayName":"张hongxu","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"01344108933923387301"}},"outputId":"6c1c0839-5463-4d33-bfd0-c154e6f35c3c"},"source":["#进行五折交叉的数据验证，设k=0看看数据处理的结果\n","k=0\n","val_df=train_df.iloc[k*40000:(k+1)*40000, ]\n","trains_df=pd.concat([train_df.iloc[:k*40000, ],train_df.iloc[(k+1)*40000:, ]])\n","val_df['summary']=val_df['text'].apply(lambda x:slipt2(x))\n","trains_df['summary']=trains_df['text'].apply(lambda x:slipt2(x))\n","val_df['len']=val_df['summary'].apply(lambda x:len(x.split(' ')))\n","\n","#加载到dataset并预处理\n","trains_ds=Dataset.from_pandas(trains_df).remove_columns(\"text\").shuffle(seed=42)\n","val_ds=Dataset.from_pandas(val_df).remove_columns(\"text\").shuffle(seed=42)\n","print(val_ds['len'])\n","print(trains_ds)"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["[510, 510, 510, 510, 510, 409, 510, 510, 462, 490, 510, 510, 358, 240, 510, 510, 510, 510, 510, 510, 510, 510, 510, 510, 476, 510, 362, 510, 510, 208, 421, 510, 52, 510, 375, 199, 510, 510, 478, 510, 381, 318, 267, 510, 395, 188, 510, 255, 510, 510, 254, 510, 510, 479, 510, 510, 413, 490, 490, 510, 75, 137, 450, 488, 510, 481, 510, 510, 510, 510, 510, 473, 510, 510, 510, 510, 510, 240, 150, 432, 510, 163, 296, 510, 186, 510, 510, 510, 172, 510, 510, 510, 510, 510, 510, 510, 462, 117, 241, 510, 506, 510, 467, 399, 510, 510, 245, 485, 344, 510, 510, 510, 510, 510, 381, 510, 507, 510, 405, 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510]\n"]}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000,"referenced_widgets":["e5ccba75043349598045d110278c0f19","120f9d55e7dc401b8a35ab2ed7c0d215","6c76d39533e64e86a3d61c24552e086c","1520a3fdee8a4797890f898bc094f816","a3d509dd31d04d888aae2c698a8abd5f","283835b50dac4aee95f16b64b6cf544c","8bc19b680cee4f78b6537f1c3f02ff4f","133d19358c3a43c08c61a42515590bac","2badc3747f7a435ababc5cd5d8110e2c","e22100d9c5a8438db2e00f8bdd518f66","355be87d6fc542a689265d94c7d5651d","2d3acfa3943b495b86ec54f98373b798","9e0000801b144944a04164a2f2fb2d81","cce94fd6983c430dafaf6b68f9b056b6","c5b3234bc8b7409a8c592dbf80258945","8c4d8b4fa5c74764b041ead6ecc8e03e","c1b0f0b834ce41c397df497d6fee9d0e","5ce3d295b582495294a6b11e2ea280e3","2fdb81f681b44965b6162d3c5a295897","7dfd09df921445a79eb58d5628cfe0f9","be3e01aa33204571a7adaf8a465548b5","e098bccca6ff4b6ba2b20bf33318e58d","d4d0a09647e44fd595533953e1ba1c3e","7242b53498c14658a9e01fe86ff9b53d","aa15fb0a9a94478688403f16b6a96649","46023e4721ca4f54ab77f0e63aa75960","ed67cd187a9844af804f15fd58af8c30","6fcb8fa40c734fd2843642301a6baa15","64ba074578b146859898fccb38852f35","32619094e0204394a0f21ec29ea6c54a","56526d370e744fc0bbaf1d41d1339a64","e8c1c991a12f4bf7b334598e4d276240","119ff591fe1c45f18cb4dbc29674110b","cb029519b5744193963121de2452d33e","17b92121b000481fa2a29d191eb890a6","804069ef0b9645eb82dc99f94bcc1559","d58b1ec18bfc466093c0acbb46be00cf","c3d69fe3a3534c28993a2a7581ab7db5","47248c3379c74ce8af852a8efcd99f13","09bc4358f18f4e6ea98224779e800918","a91e33dfde974150bafdb33d9ff382cd","69fc8e02b2344febbb0b20e4d9539b77","7c8adf572d4848ad96b1850588b43c91","2904f741bc27412496be85da6d5a5b40","24014dcd7c5948edb5743e847df4b949","24732aff40d2492986c6be8d197e8088","990ec28342db4fe5989c6f344956a2e9","12c70a453a1e4ff48a045703d2f86e22","4a4ac6cca14c4f94943611ffdcd1970f","f43533a29a144c90b8b875dedd6b19c1","a879edef93b44452a2cb067306bdd276","d909116dbe194d398fbe1209dc874265","44c9a93811844a2f99451500cefe807e","136cda00875a4e77ad05ebc0e9c21c24","113c3d385d0f40fe9a323b61de16ce8d","213d3525c92243c3be719a3078f17e30","003063af51fb47c8b6623064513d908d","0ee1b22ccebe4e7c9bb846769bd67631","300ca6fde0254e34b1ce73497208a456","1bd3999467024e5fb4cf385219a67dde","c0d934c5ed364f5aa622c40dd2422613","ec66933dd0a5403498e3f0e2f7d23f8c","47d4c53833264365a28048ac9c8df64a","509529f2ba924ce68efd5eda40f1afb8","f9bd2f4f6d5545b8b190b3c10eb29495","e2e1d3046c7142f9882a7eda844952d2","c540e292690f43738462ce4424f778d6","d22a9da52a4e44f6a1368968323db3b2","5ca0f868858645e2a4971962b4307c29","51e70b21386e4ab0966712ab805be4e0","b4e5254c3390491abc15757c570df830","4f86fb63a4054de9aab897c8f7bab7d8","d8060d14bddb4c198a441f0c115b8307","45576cfb84b94d5f8e8d6acffc429378","effa5f0a21fb4a5ca2dbd30dfb76efd4","2e58a461fb3a4d6ead40000847440c5e","08dad2c95bd9439a80038a1240ba4d56","d842722dfb2d480e905353ea4d617ca8","5f2f9145d98e416284b0d3663ff0491b","7db4c07349c147de98077ba9d6223d27","4f958297e96044c889569d3c6ab4ece2","c083a9eb014f4b5cbd8ebb58cbe6d855","e838b01890b141948a20b3f1c79bb6c1","624c032cd53844e7be0d12dd015c95c6","fbda8956039b48a19b59bf113e8b64ff","b5ca7ff5ac1e4e70b7e07574ac37a84a","3fb3588d7d7d4e1fbfe393a382343426","363eb1706e5f43ca8539e5031135c73f","bf619d2b527842ccaac8a00d433d521d","737ef61105b349d8902ec216f9eecd2c","afc95c299e1a4a40b05f169f2621153e","ac89e210abf34d51bdce177d6e864904","f33dc5ca95d54d3a8e5a07bc5903487a","9f87f7c7b80c414fb2952821380e4690","165d89901d454dc4871d51db7a508fca","3c3a9c6456654d3db27ec88bde948ccd","e2fe6f9f6aee4cb98a9abeae1e97888d","38439318313146b5baf83a9a8002e00f","809cdc80485f4fe4ab4ee63693578c9b","45ef73f68d304e5c8edbf45f847eb592","980532ca9af741b6975c3314d76902e6","09f6a87d342940e7871c17ab7c293ce9","aea607ce0ce042f49ba72c7dbf8934fa","313bdae82404462c881e65b68a261cc0","306e631019ea479088f9ea808a9f3ed0","33637ba3d19e4f758a20de5ee05afd6f","3c528071e49d4fb4ab0ffdbae96a94ca","eaa99ccca2cc4f30b535d3f078e53a04","ccca966e6ea04276b7702c2a99afa89b","09d87766613b4692bad719032c2af7fc"]},"id":"79vaC6bwzOeZ","executionInfo":{"status":"ok","timestamp":1634762929682,"user_tz":-480,"elapsed":2152074,"user":{"displayName":"张hongxu","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"01344108933923387301"}},"outputId":"47f09a12-eb3e-4d1c-a4ef-69f93261f30a"},"source":["#五折交叉验证\n","for k in range(5):\n","  val_df=train_df.iloc[k*40000:(k+1)*40000, ]\n","  trains_df=pd.concat([train_df.iloc[:k*40000, ],train_df.iloc[(k+1)*40000:, ]])\n","\n","  val_df['summary']=val_df['text'].apply(lambda x:slipt2(x))\n","  trains_df['summary']=trains_df['text'].apply(lambda x:slipt2(x))\n","\n","#加载到dataset并预处理\n","  trains_ds=Dataset.from_pandas(trains_df).remove_columns(\"text\").shuffle(seed=42)\n","  val_ds=Dataset.from_pandas(val_df).remove_columns(\"text\").shuffle(seed=42)\n"," \n","  tokenized_trains_ds=trains_ds.map(lambda examples:fast_tokenizer(examples['summary'],truncation=True,padding=True),batched=True)\n","  tokenized_val_ds=val_ds.map(lambda examples:fast_tokenizer(examples['summary'],truncation=True,padding=True),batched=True)\n","\n","  path=(\"news-cla\"+str(k))\n","\n","  #进行任务微调\n","  args=TrainingArguments(\n","    output_dir=path,\n","    evaluation_strategy=\"epoch\",\n","    save_strategy=\"epoch\",\n","    learning_rate=2e-5,\n","    per_device_train_batch_size=96,\n","    per_device_eval_batch_size=96,\n","    num_train_epochs=1,\n","    weight_decay=0.01,)\n","  \n","  trainer=Trainer(\n","    model,\n","    args,\n","    train_dataset=tokenized_trains_ds,\n","    eval_dataset=tokenized_val_ds,\n","    tokenizer=fast_tokenizer,\n","    compute_metrics=compute_metrics)\n","  trainer.train()"],"execution_count":null,"outputs":[{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  \n"]},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"e5ccba75043349598045d110278c0f19","version_major":2,"version_minor":0},"text/plain":["  0%|          | 0/160 [00:00<?, ?ba/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"2d3acfa3943b495b86ec54f98373b798","version_major":2,"version_minor":0},"text/plain":["  0%|          | 0/40 [00:00<?, ?ba/s]"]},"metadata":{},"output_type":"display_data"},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["PyTorch: setting up devices\n","The default value for the training argument `--report_to` will change in v5 (from all installed integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as now. You should start updating your code and make this info disappear :-).\n","The following columns in the training set  don't have a corresponding argument in `BertForSequenceClassification.forward` and have been ignored: summary.\n","***** Running training *****\n","  Num examples = 160000\n","  Num Epochs = 1\n","  Instantaneous batch size per device = 96\n","  Total train batch size (w. parallel, distributed & accumulation) = 96\n","  Gradient Accumulation steps = 1\n","  Total optimization steps = 1667\n"]},{"data":{"text/html":["\n","    <div>\n","      \n","      <progress value='1667' max='1667' style='width:300px; height:20px; vertical-align: middle;'></progress>\n","      [1667/1667 15:29, Epoch 1/1]\n","    </div>\n","    <table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: left;\">\n","      <th>Epoch</th>\n","      <th>Training Loss</th>\n","      <th>Validation Loss</th>\n","      <th>Accuracy</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <td>1</td>\n","      <td>0.203200</td>\n","      <td>0.182172</td>\n","      <td>0.945925</td>\n","    </tr>\n","  </tbody>\n","</table><p>"],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{},"output_type":"display_data"},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["The following columns in the evaluation set  don't have a corresponding argument in `BertForSequenceClassification.forward` and have been ignored: summary.\n","***** Running Evaluation *****\n","  Num examples = 40000\n","  Batch size = 96\n","Saving model checkpoint to news-cla0/checkpoint-1667\n","Configuration saved in news-cla0/checkpoint-1667/config.json\n","Model weights saved in news-cla0/checkpoint-1667/pytorch_model.bin\n","tokenizer config file saved in news-cla0/checkpoint-1667/tokenizer_config.json\n","Special tokens file saved in news-cla0/checkpoint-1667/special_tokens_map.json\n","\n","\n","Training completed. Do not forget to share your model on huggingface.co/models =)\n","\n","\n","/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  \n"]},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"d4d0a09647e44fd595533953e1ba1c3e","version_major":2,"version_minor":0},"text/plain":["  0%|          | 0/160 [00:00<?, ?ba/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"cb029519b5744193963121de2452d33e","version_major":2,"version_minor":0},"text/plain":["  0%|          | 0/40 [00:00<?, ?ba/s]"]},"metadata":{},"output_type":"display_data"},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["PyTorch: setting up devices\n","The default value for the training argument `--report_to` will change in v5 (from all installed integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as now. You should start updating your code and make this info disappear :-).\n","The following columns in the training set  don't have a corresponding argument in `BertForSequenceClassification.forward` and have been ignored: summary, __index_level_0__.\n","***** Running training *****\n","  Num examples = 160000\n","  Num Epochs = 1\n","  Instantaneous batch size per device = 96\n","  Total train batch size (w. parallel, distributed & accumulation) = 96\n","  Gradient Accumulation steps = 1\n","  Total optimization steps = 1667\n"]},{"data":{"text/html":["\n","    <div>\n","      \n","      <progress value='1667' max='1667' style='width:300px; height:20px; vertical-align: middle;'></progress>\n","      [1667/1667 14:40, Epoch 1/1]\n","    </div>\n","    <table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: left;\">\n","      <th>Epoch</th>\n","      <th>Training Loss</th>\n","      <th>Validation Loss</th>\n","      <th>Accuracy</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <td>1</td>\n","      <td>0.167000</td>\n","      <td>0.146571</td>\n","      <td>0.955250</td>\n","    </tr>\n","  </tbody>\n","</table><p>"],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{},"output_type":"display_data"},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["The following columns in the evaluation set  don't have a corresponding argument in `BertForSequenceClassification.forward` and have been ignored: summary.\n","***** Running Evaluation *****\n","  Num examples = 40000\n","  Batch size = 96\n","Saving model checkpoint to news-cla1/checkpoint-1667\n","Configuration saved in news-cla1/checkpoint-1667/config.json\n","Model weights saved in news-cla1/checkpoint-1667/pytorch_model.bin\n","tokenizer config file saved in news-cla1/checkpoint-1667/tokenizer_config.json\n","Special tokens file saved in news-cla1/checkpoint-1667/special_tokens_map.json\n","\n","\n","Training completed. Do not forget to share your model on huggingface.co/models =)\n","\n","\n","/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  \n"]},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"24014dcd7c5948edb5743e847df4b949","version_major":2,"version_minor":0},"text/plain":["  0%|          | 0/160 [00:00<?, ?ba/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"213d3525c92243c3be719a3078f17e30","version_major":2,"version_minor":0},"text/plain":["  0%|          | 0/40 [00:00<?, ?ba/s]"]},"metadata":{},"output_type":"display_data"},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["PyTorch: setting up devices\n","The default value for the training argument `--report_to` will change in v5 (from all installed integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as now. You should start updating your code and make this info disappear :-).\n","The following columns in the training set  don't have a corresponding argument in `BertForSequenceClassification.forward` and have been ignored: summary, __index_level_0__.\n","***** Running training *****\n","  Num examples = 160000\n","  Num Epochs = 1\n","  Instantaneous batch size per device = 96\n","  Total train batch size (w. parallel, distributed & accumulation) = 96\n","  Gradient Accumulation steps = 1\n","  Total optimization steps = 1667\n"]},{"data":{"text/html":["\n","    <div>\n","      \n","      <progress value='1668' max='1667' style='width:300px; height:20px; vertical-align: middle;'></progress>\n","      [1667/1667 12:06, Epoch 1/1]\n","    </div>\n","    <table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: left;\">\n","      <th>Epoch</th>\n","      <th>Training Loss</th>\n","      <th>Validation Loss</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","  </tbody>\n","</table><p>\n","    <div>\n","      \n","      <progress value='415' max='417' style='width:300px; height:20px; vertical-align: middle;'></progress>\n","      [415/417 02:33 < 00:00, 2.70 it/s]\n","    </div>\n","    "],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{},"output_type":"display_data"},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["The following columns in the evaluation set  don't have a corresponding argument in `BertForSequenceClassification.forward` and have been ignored: summary.\n","***** Running Evaluation *****\n","  Num examples = 40000\n","  Batch size = 96\n"]},{"output_type":"display_data","data":{"text/html":["\n","    <div>\n","      \n","      <progress value='1667' max='1667' style='width:300px; height:20px; vertical-align: middle;'></progress>\n","      [1667/1667 14:46, Epoch 1/1]\n","    </div>\n","    <table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: left;\">\n","      <th>Epoch</th>\n","      <th>Training Loss</th>\n","      <th>Validation Loss</th>\n","      <th>Accuracy</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <td>1</td>\n","      <td>0.150700</td>\n","      <td>0.129751</td>\n","      <td>0.960875</td>\n","    </tr>\n","  </tbody>\n","</table><p>"],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["Saving model checkpoint to news-cla2/checkpoint-1667\n","Configuration saved in news-cla2/checkpoint-1667/config.json\n","Model weights saved in news-cla2/checkpoint-1667/pytorch_model.bin\n","tokenizer config file saved in news-cla2/checkpoint-1667/tokenizer_config.json\n","Special tokens file saved in news-cla2/checkpoint-1667/special_tokens_map.json\n","\n","\n","Training completed. Do not forget to share your model on huggingface.co/models =)\n","\n","\n","/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  \n"]},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"c540e292690f43738462ce4424f778d6","version_minor":0,"version_major":2},"text/plain":["  0%|          | 0/160 [00:00<?, ?ba/s]"]},"metadata":{}},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"d842722dfb2d480e905353ea4d617ca8","version_minor":0,"version_major":2},"text/plain":["  0%|          | 0/40 [00:00<?, ?ba/s]"]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["PyTorch: setting up devices\n","The default value for the training argument `--report_to` will change in v5 (from all installed integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as now. You should start updating your code and make this info disappear :-).\n","The following columns in the training set  don't have a corresponding argument in `BertForSequenceClassification.forward` and have been ignored: summary, __index_level_0__.\n","***** Running training *****\n","  Num examples = 160000\n","  Num Epochs = 1\n","  Instantaneous batch size per device = 96\n","  Total train batch size (w. parallel, distributed & accumulation) = 96\n","  Gradient Accumulation steps = 1\n","  Total optimization steps = 1667\n"]},{"output_type":"display_data","data":{"text/html":["\n","    <div>\n","      \n","      <progress value='1667' max='1667' style='width:300px; height:20px; vertical-align: middle;'></progress>\n","      [1667/1667 14:41, Epoch 1/1]\n","    </div>\n","    <table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: left;\">\n","      <th>Epoch</th>\n","      <th>Training Loss</th>\n","      <th>Validation Loss</th>\n","      <th>Accuracy</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <td>1</td>\n","      <td>0.137200</td>\n","      <td>0.117883</td>\n","      <td>0.964200</td>\n","    </tr>\n","  </tbody>\n","</table><p>"],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["The following columns in the evaluation set  don't have a corresponding argument in `BertForSequenceClassification.forward` and have been ignored: summary.\n","***** Running Evaluation *****\n","  Num examples = 40000\n","  Batch size = 96\n","Saving model checkpoint to news-cla3/checkpoint-1667\n","Configuration saved in news-cla3/checkpoint-1667/config.json\n","Model weights saved in news-cla3/checkpoint-1667/pytorch_model.bin\n","tokenizer config file saved in news-cla3/checkpoint-1667/tokenizer_config.json\n","Special tokens file saved in news-cla3/checkpoint-1667/special_tokens_map.json\n","\n","\n","Training completed. Do not forget to share your model on huggingface.co/models =)\n","\n","\n","/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  \n"]},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"bf619d2b527842ccaac8a00d433d521d","version_minor":0,"version_major":2},"text/plain":["  0%|          | 0/160 [00:00<?, ?ba/s]"]},"metadata":{}},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"45ef73f68d304e5c8edbf45f847eb592","version_minor":0,"version_major":2},"text/plain":["  0%|          | 0/40 [00:00<?, ?ba/s]"]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["PyTorch: setting up devices\n","The default value for the training argument `--report_to` will change in v5 (from all installed integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as now. You should start updating your code and make this info disappear :-).\n","The following columns in the training set  don't have a corresponding argument in `BertForSequenceClassification.forward` and have been ignored: summary.\n","***** Running training *****\n","  Num examples = 160000\n","  Num Epochs = 1\n","  Instantaneous batch size per device = 96\n","  Total train batch size (w. parallel, distributed & accumulation) = 96\n","  Gradient Accumulation steps = 1\n","  Total optimization steps = 1667\n"]},{"output_type":"display_data","data":{"text/html":["\n","    <div>\n","      \n","      <progress value='1667' max='1667' style='width:300px; height:20px; vertical-align: middle;'></progress>\n","      [1667/1667 14:41, Epoch 1/1]\n","    </div>\n","    <table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: left;\">\n","      <th>Epoch</th>\n","      <th>Training Loss</th>\n","      <th>Validation Loss</th>\n","      <th>Accuracy</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <td>1</td>\n","      <td>0.127500</td>\n","      <td>0.102261</td>\n","      <td>0.968400</td>\n","    </tr>\n","  </tbody>\n","</table><p>"],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["The following columns in the evaluation set  don't have a corresponding argument in `BertForSequenceClassification.forward` and have been ignored: summary.\n","***** Running Evaluation *****\n","  Num examples = 40000\n","  Batch size = 96\n","Saving model checkpoint to news-cla4/checkpoint-1667\n","Configuration saved in news-cla4/checkpoint-1667/config.json\n","Model weights saved in news-cla4/checkpoint-1667/pytorch_model.bin\n","tokenizer config file saved in news-cla4/checkpoint-1667/tokenizer_config.json\n","Special tokens file saved in news-cla4/checkpoint-1667/special_tokens_map.json\n","\n","\n","Training completed. Do not forget to share your model on huggingface.co/models =)\n","\n","\n"]}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"y3XBXO18faB5","executionInfo":{"status":"ok","timestamp":1634231724073,"user_tz":-480,"elapsed":1006,"user":{"displayName":"张hongxu","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"01344108933923387301"}},"outputId":"57091473-cf74-4ff2-c5ff-82460d5bb280"},"source":["trainer.save_model(\"./EsperBERTo\")"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["Saving model checkpoint to ./EsperBERTo\n","Configuration saved in ./EsperBERTo/config.json\n","Model weights saved in ./EsperBERTo/pytorch_model.bin\n","tokenizer config file saved in ./EsperBERTo/tokenizer_config.json\n","Special tokens file saved in ./EsperBERTo/special_tokens_map.json\n"]}]},{"cell_type":"code","metadata":{"id":"kCJUJldAZF4w"},"source":["trainer.evaluate()"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"KfDv-o3Kl1ru","colab":{"base_uri":"https://localhost:8080/","height":183,"referenced_widgets":["79aa4cb1fce346c7b71dd3ffa377cfd7","07afc78c10484c75ae63a21eadae3515","7cd35108b0df42d48ed5dda7989053ca","2f4360118c5547439f286d4ed18d91fb","f89073ea38b04231b497c81d3111d899","43d40e1ac7fe4b3384405ae7cb4c81df","72cb4bac116545e1a759a38c87b60e63","50fed50b6b674f14b7cf98d80c8c4d40","c7cfd60468d74bddacda546307aaaf78","a47d57666be944e79bc1229b7efcaadd","36d55298cb664051a3d0c2a89e3beeba","1d6a7fc7962e476c96f757c61c505b1d","b49627132f6849558b08366286294046"]},"executionInfo":{"status":"ok","timestamp":1634229533470,"user_tz":-480,"elapsed":8377,"user":{"displayName":"张hongxu","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"01344108933923387301"}},"outputId":"1acfac0e-c455-48e4-9fad-6154f7f483c4"},"source":["#读取测试集\n","from datasets import load_dataset\n","dataset=load_dataset('csv',data_files='./test_a.csv')"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["Using custom data configuration default-e11314c8484ef22e\n"]},{"output_type":"stream","name":"stdout","text":["Downloading and preparing dataset csv/default to /root/.cache/huggingface/datasets/csv/default-e11314c8484ef22e/0.0.0/bf68a4c4aefa545d0712b2fcbb1b327f905bbe2f6425fbc5e8c25234acb9e14a...\n"]},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"79aa4cb1fce346c7b71dd3ffa377cfd7","version_minor":0,"version_major":2},"text/plain":["  0%|          | 0/1 [00:00<?, ?it/s]"]},"metadata":{}},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"1d6a7fc7962e476c96f757c61c505b1d","version_minor":0,"version_major":2},"text/plain":["  0%|          | 0/1 [00:00<?, ?it/s]"]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Dataset csv downloaded and prepared to /root/.cache/huggingface/datasets/csv/default-e11314c8484ef22e/0.0.0/bf68a4c4aefa545d0712b2fcbb1b327f905bbe2f6425fbc5e8c25234acb9e14a. Subsequent calls will reuse this data.\n"]},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"b49627132f6849558b08366286294046","version_minor":0,"version_major":2},"text/plain":["  0%|          | 0/1 [00:00<?, ?it/s]"]},"metadata":{}}]},{"cell_type":"code","metadata":{"id":"CEm7hIZV63zC","colab":{"base_uri":"https://localhost:8080/","height":49,"referenced_widgets":["503f6ec93a2c4a26a91109beb19aafc1","11cd9e4d5b744fdaba204e0db2070dce","c391b40a0d0342d7bb50622f9b10523b","25483170561b41ef9265ac6b1b3c84e1","1fa00084a05b469aaf852b7702ae6bee","55118c9c881049088f9df1e406bf01d1","a17ea21241ae4110b93cbd44b8df5241","af344d7ff4524b3b9ad3ba6b774e468e","5ac045ea78074b2a8047a398832e212b","c0c62b246edc48a5b3f0ada7a4932bce","dda375b409904b63909854e351b3338b"]},"executionInfo":{"status":"ok","timestamp":1634229696062,"user_tz":-480,"elapsed":88022,"user":{"displayName":"张hongxu","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"01344108933923387301"}},"outputId":"7473eaed-701a-4ac9-fa3e-5a22fb15c5e9"},"source":["#数据预处理\n","def tokenize_function(examples):\n","    return fast_tokenizer (examples[\"text\"],truncation=True,padding=True)\n","tokenized_datasets=dataset.map(tokenize_function,batched=True)"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"503f6ec93a2c4a26a91109beb19aafc1","version_minor":0,"version_major":2},"text/plain":["  0%|          | 0/50 [00:00<?, ?ba/s]"]},"metadata":{}}]},{"cell_type":"markdown","metadata":{"id":"hJ_mdP_48V_R"},"source":[""]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"GaJUrAjtrt7m","executionInfo":{"status":"ok","timestamp":1634229742522,"user_tz":-480,"elapsed":331,"user":{"displayName":"张hongxu","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"01344108933923387301"}},"outputId":"79d55127-7bd6-4277-e39d-153ae5e35ea6"},"source":["tokenized_datasets"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["DatasetDict({\n","    train: Dataset({\n","        features: ['attention_mask', 'input_ids', 'text', 'token_type_ids'],\n","        num_rows: 50000\n","    })\n","})"]},"metadata":{},"execution_count":63}]},{"cell_type":"code","metadata":{"id":"JGrY8XTtJagI","colab":{"base_uri":"https://localhost:8080/","height":322},"executionInfo":{"status":"ok","timestamp":1634231841898,"user_tz":-480,"elapsed":103240,"user":{"displayName":"张hongxu","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"01344108933923387301"}},"outputId":"edce1b66-28b1-473f-c5df-653ca8f71306"},"source":["predictions,metrics,loss=trainer.predict(tokenized_datasets['train'],metric_key_prefix=\"predict\")\n","predictions"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["The following columns in the test set  don't have a corresponding argument in `BertForSequenceClassification.forward` and have been ignored: text.\n","***** Running Prediction *****\n","  Num examples = 50000\n","  Batch size = 96\n"]},{"output_type":"display_data","data":{"text/html":["\n","    <div>\n","      \n","      <progress value='521' max='521' style='width:300px; height:20px; vertical-align: middle;'></progress>\n","      [521/521 01:41]\n","    </div>\n","    "],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{}},{"output_type":"execute_result","data":{"text/plain":["array([[ 1.8426243 ,  8.174837  , -1.2704638 , ..., -1.320911  ,\n","        -1.929365  , -2.486765  ],\n","       [-0.75752604, -1.2208161 ,  9.0089445 , ..., -1.2063974 ,\n","         0.7124819 , -1.1222411 ],\n","       [ 0.6969581 , -0.6338869 , -0.73178047, ...,  0.06477395,\n","        -1.4471081 , -2.1442966 ],\n","       ...,\n","       [-0.39861476,  6.7122326 , -1.4422836 , ..., -1.4204785 ,\n","        -1.1205809 , -2.2244375 ],\n","       [-0.83510345, -0.96118903,  0.43861002, ..., -0.11299364,\n","        -1.0912285 , -2.1063776 ],\n","       [ 0.75804245,  8.388309  , -1.4211463 , ..., -1.3522512 ,\n","        -1.7220726 , -2.6125865 ]], dtype=float32)"]},"metadata":{},"execution_count":74}]},{"cell_type":"code","metadata":{"id":"GWSDrSb-Peh1","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1634231849613,"user_tz":-480,"elapsed":319,"user":{"displayName":"张hongxu","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"01344108933923387301"}},"outputId":"e0c1a18c-a111-4197-dc09-b738f5b42510"},"source":["loss"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'predict_runtime': 103.1959,\n"," 'predict_samples_per_second': 484.515,\n"," 'predict_steps_per_second': 5.049}"]},"metadata":{},"execution_count":76}]},{"cell_type":"code","metadata":{"id":"X0_c_hr2ViA5","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1634231846029,"user_tz":-480,"elapsed":276,"user":{"displayName":"张hongxu","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"01344108933923387301"}},"outputId":"d945e145-cc5c-45e7-c910-3b0744fe9d19"},"source":["pred=np.argmax(predictions,axis=1)\n","pred"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([1, 2, 8, ..., 1, 3, 1])"]},"metadata":{},"execution_count":75}]},{"cell_type":"code","metadata":{"id":"2uKUyeBfQ1dh"},"source":["pd.DataFrame({'label':pred}).to_csv('submit1015_3.csv',index=None)"],"execution_count":null,"outputs":[]}]}